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Add a new unit test for mixed lazy results

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This test validates that when an output list is fed to a lazy node, the
node will properly evaluate previous nodes that are needed by any inputs
to the lazy node.

No code in the execution model has been changed. The test already
passes.
This commit is contained in:
Jacob Segal 2024-08-01 20:24:08 -07:00
parent bb5de4dfd4
commit c4666bf7db
1 changed files with 17 additions and 0 deletions
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17
tests/inference/test_execution.py
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@ -413,6 +413,23 @@ class TestExecution:
for i in range(3):
assert numpy.array(images_literal[i]).min() == 255 and numpy.array(images_literal[i]).max() == 255, "All images should be white"
def test_mixed_lazy_results(self, client: ComfyClient, builder: GraphBuilder):
g = builder
val_list = g.node("TestMakeListNode", value1=0.0, value2=0.5, value3=1.0)
mask = g.node("StubMask", value=val_list.out(0), height=512, width=512, batch_size=1)
input1 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1)
input2 = g.node("StubImage", content="WHITE", height=512, width=512, batch_size=1)
mix = g.node("TestLazyMixImages", image1=input1.out(0), image2=input2.out(0), mask=mask.out(0))
rebatch = g.node("RebatchImages", images=mix.out(0), batch_size=3)
output = g.node("SaveImage", images=rebatch.out(0))
result = client.run(g)
images = result.get_images(output)
assert len(images) == 3, "Should have 3 image"
assert numpy.array(images[0]).min() == 0 and numpy.array(images[0]).max() == 0, "First image should be 0.0"
assert numpy.array(images[1]).min() == 127 and numpy.array(images[1]).max() == 127, "Second image should be 0.5"
assert numpy.array(images[2]).min() == 255 and numpy.array(images[2]).max() == 255, "Third image should be 1.0"
def test_output_reuse(self, client: ComfyClient, builder: GraphBuilder):
g = builder
input1 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1)